Fast and Resource-Efficient Object Tracking on Edge Devices: A Measurement Study

• URL: http://arxiv.org/abs/2309.02666v1
• Date: Wed, 6 Sep 2023 02:25:36 GMT
• Title: Fast and Resource-Efficient Object Tracking on Edge Devices: A Measurement Study
• Authors: Sanjana Vijay Ganesh, Yanzhao Wu, Gaowen Liu, Ramana Kompella, Ling Liu
• Abstract summary: Multi-object tracking (MOT) detects the moving objects and tracks their locations frame by frame as real scenes are being captured into a video. This paper examines the performance issues and edge-specific optimization opportunities for object tracking. We present several edge specific performance optimization strategies, collectively coined as EMO, to speed up the real time object tracking.
• Score: 9.976630547252427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object tracking is an important functionality of edge video analytic systems and services. Multi-object tracking (MOT) detects the moving objects and tracks their locations frame by frame as real scenes are being captured into a video. However, it is well known that real time object tracking on the edge poses critical technical challenges, especially with edge devices of heterogeneous computing resources. This paper examines the performance issues and edge-specific optimization opportunities for object tracking. We will show that even the well trained and optimized MOT model may still suffer from random frame dropping problems when edge devices have insufficient computation resources. We present several edge specific performance optimization strategies, collectively coined as EMO, to speed up the real time object tracking, ranging from window-based optimization to similarity based optimization. Extensive experiments on popular MOT benchmarks demonstrate that our EMO approach is competitive with respect to the representative methods for on-device object tracking techniques in terms of run-time performance and tracking accuracy. EMO is released on Github at https://github.com/git-disl/EMO.

Related papers

• Temporal Correlation Meets Embedding: Towards a 2nd Generation of JDE-based Real-Time Multi-Object Tracking [52.04679257903805]
  Joint Detection and Embedding (JDE) trackers have demonstrated excellent performance in Multi-Object Tracking (MOT) tasks. Our tracker, named TCBTrack, achieves state-of-the-art performance on multiple public benchmarks.
  arXiv  Detail & Related papers  (2024-07-19T07:48:45Z)
• Exploring Dynamic Transformer for Efficient Object Tracking [58.120191254379854]
  We propose DyTrack, a dynamic transformer framework for efficient tracking. DyTrack automatically learns to configure proper reasoning routes for various inputs, gaining better utilization of the available computational budget. Experiments on multiple benchmarks demonstrate that DyTrack achieves promising speed-precision trade-offs with only a single model.
  arXiv  Detail & Related papers  (2024-03-26T12:31:58Z)
• SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features [52.213656737672935]
  SpikeMOT is an event-based multi-object tracker. SpikeMOT uses spiking neural networks to extract sparsetemporal features from event streams associated with objects.
  arXiv  Detail & Related papers  (2023-09-29T05:13:43Z)
• UnsMOT: Unified Framework for Unsupervised Multi-Object Tracking with Geometric Topology Guidance [6.577227592760559]
  UnsMOT is a novel framework that combines appearance and motion features of objects with geometric information to provide more accurate tracking. Experimental results show remarkable performance in terms of HOTA, IDF1, and MOTA metrics in comparison with state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-03T04:58:12Z)
• End-to-end Tracking with a Multi-query Transformer [96.13468602635082]
  Multiple-object tracking (MOT) is a challenging task that requires simultaneous reasoning about location, appearance, and identity of the objects in the scene over time. Our aim in this paper is to move beyond tracking-by-detection approaches, to class-agnostic tracking that performs well also for unknown object classes.
  arXiv  Detail & Related papers  (2022-10-26T10:19:37Z)
• Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems [93.73198973454944]
  Two popular voxel-based 3D object detection methods are studied in this paper. Our experiments show that these methods mostly fail to detect distant small objects due to the sparsity of the input point clouds at large distances. Our findings suggest that a considerable part of the computations of existing methods is focused on locations of the scene that do not contribute with successful detection.
  arXiv  Detail & Related papers  (2021-05-21T12:40:59Z)
• ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles [19.41234144545467]
  We introduce ApproxDet, an adaptive video object detection framework for mobile devices to meet accuracy-latency requirements. We evaluate ApproxDet on a large benchmark video dataset and compare quantitatively to AdaScale and YOLOv3. We find that ApproxDet is able to adapt to a wide variety of contention and content characteristics and outshines all baselines.
  arXiv  Detail & Related papers  (2020-10-21T04:11:05Z)
• Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking [87.0297771292994]
  We propose an Event-based Tracking-by-Detection (ETD) method for generic bounding box-based object tracking. To achieve this goal, we present an Adaptive Time-Surface with Linear Time Decay (ATSLTD) event-to-frame conversion algorithm. We compare the proposed ETD method with seven popular object tracking methods, that are based on conventional cameras or event cameras, and two variants of ETD.
  arXiv  Detail & Related papers  (2020-02-13T15:58:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.